title: Practical quantification of necrosis in histological whole-slide images.
abstract: Since the histological quantification of necrosis is a common task in medical research and practice, we evaluate different image analysis methods for quantifying necrosis in whole-slide images. In a practical usage scenario, we assess the impact of different classification algorithms and feature sets on both accuracy and computation time. We show how a well-chosen combination of multiresolution features and an efficient postprocessing step enables the accurate quantification necrosis in gigapixel images in less than a minute. The results are general enough to be applied to other areas of histological image analysis as well.
authors.affiliation: Fraunhofer MEVIS, Universitätsallee 29, 28359 Bremen, Germany. andre.homeyer@mevis.fraunhofer.de
authors.forename: André
authors.initials: A
authors.lastname: Homeyer
authors.forename: Andrea
authors.initials: A
authors.lastname: Schenk
authors.forename: Janine
authors.initials: J
authors.lastname: Arlt
authors.forename: Uta
authors.initials: U
authors.lastname: Dahmen
authors.forename: Olaf
authors.initials: O
authors.lastname: Dirsch
authors.forename: Horst K
authors.initials: HK
authors.lastname: Hahn
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008853:Microscopy / Q000379:methods*; D009336:Necrosis / Q000473:pathology*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity
keywords: Histological image analysis; Necrosis quantification; Pattern recognition; Whole-slide imaging